| year | age_cat | mort_count | pop_count | rate_pc | avg5yr | proj_avg |
|---|---|---|---|---|---|---|
| 2010 | 0-4 | 327 | 347000 | 0.09424 | 213 | NA |
| 2010 | 5-14 | 55 | 610700 | 0.00901 | 46 | NA |
| 2010 | 15-24 | 261 | 615700 | 0.04239 | 170 | NA |
| 2010 | 25-34 | 449 | 762800 | 0.05886 | 333 | NA |
| 2010 | 35-44 | 703 | 681500 | 0.10315 | 644 | NA |
| 2010 | 45-54 | 1425 | 568300 | 0.25075 | 1347 | NA |
Exploratory Data Analysis
Mortality Rates
Mortality rate by age category
Calculate mortality rate percentage for each age category
Excess Mortality
Method 1: Deaths in excess of the average
Number of Deaths above Average
Percentage Deaths above Average by Age Group
| year | age_cat | mort_count | xs_avg_pc |
|---|---|---|---|
| 2020 | 0-4 | 180 | -15.49 |
| 2020 | 5-14 | 47 | 2.17 |
| 2020 | 15-24 | 136 | -20.00 |
| 2020 | 25-34 | 256 | -23.12 |
| 2020 | 35-44 | 642 | -0.31 |
| 2020 | 45-54 | 1299 | -3.56 |
| 2020 | 55-64 | 2765 | -1.46 |
| 2020 | 65-74 | 5822 | 6.77 |
| 2020 | 75+ | 20618 | 5.41 |
| 2021 | 0-4 | 194 | -8.92 |
Method 2A: Difference from 2019 baseline
The baseline level is the 2019 numbers for each age group. Calculate the difference from the baseline, expressed as a percentage.
| year | age_cat | mort_count | pop_count | rate_pc | avg5yr | proj_avg | base_pc | proj_base |
|---|---|---|---|---|---|---|---|---|
| 2010 | 0-4 | 327 | 347000 | 0.09424 | 213 | NA | 0.06948 | NA |
| 2010 | 5-14 | 55 | 610700 | 0.00901 | 46 | NA | 0.00649 | NA |
| 2010 | 15-24 | 261 | 615700 | 0.04239 | 170 | NA | 0.02476 | NA |
| 2010 | 25-34 | 449 | 762800 | 0.05886 | 333 | NA | 0.04919 | NA |
| 2010 | 35-44 | 703 | 681500 | 0.10315 | 644 | NA | 0.08663 | NA |
| 2010 | 45-54 | 1425 | 568300 | 0.25075 | 1347 | NA | 0.20768 | NA |
Death Rate and Baseline Death Rate
Excess Death Rate
Method 2C: Difference from linear trend
Age group“5-14” has low \(r^2\) value (0.42) and so, a linear fit is unwarranted.
Linear Models for Death Rate Progression
Calculate difference between death rate and value predicted by linear models, as a percentage
Fitted and Predicted Values
| year | age_cat | rate_pc | fitted_values | predicted |
|---|---|---|---|---|
| 2010 | 0-4 | 0.09424 | 0.08841 | NA |
| 2010 | 15-24 | 0.04239 | 0.04743 | NA |
| 2010 | 25-34 | 0.05886 | 0.06166 | NA |
| 2010 | 35-44 | 0.10315 | 0.10716 | NA |
| 2010 | 45-54 | 0.25075 | 0.25650 | NA |
| 2010 | 55-64 | 0.61927 | 0.63922 | NA |
Observed Data and Linear Model Comparison
Excess Death Rate
Excess Mortality. Age-group comparison.
Method 1
Method 2A: Difference from 2019 baseline
Method 2C: Difference from Trend
Comparison of Excess Mortality Methods
The age group 5-14 is not given for Method 2C since there is not a linear trend in the observed data. Consequently, applying a linear model is inappropriate in this case
Vaccinations
This analysis uses data from the COVID-19 Vaccine Tracker. This covers all weeks from w53 2020 up to w39 2023. It records the median cumulative uptake (%) of the primary course by age group. It covers the 144 weeks over this time period
The main “Data on COVID-19 vaccination in the EU/EEA” data-set only covers doses administered during the most recent weeks.
Correct Date format.
Warning: package 'ISOweek' was built under R version 4.3.3Warning: package 'collapse' was built under R version 4.3.3collapse 2.0.13, see ?`collapse-package` or ?`collapse-documentation`
Attaching package: 'collapse'The following object is masked from 'package:lubridate':
is.DateThe following object is masked from 'package:tidyr':
replace_naThe following object is masked from 'package:stats':
D[1] "2015-02-26"Fit a Logistic Function to the percentage vaccinated.
Appendix
Linear Fit of Death Rate for years 2010 to 2019
